eclMuMuEAlgorithm Class Reference

Calibrate ecl crystals using muon pair events. More...

#include <eclMuMuEAlgorithm.h>

Inheritance diagram for eclMuMuEAlgorithm:

Collaboration diagram for eclMuMuEAlgorithm:

Public Types
enum	EResult {   c_OK ,   c_Iterate ,   c_NotEnoughData ,   c_Failure ,   c_Undefined }
	The result of calibration. More...

Public Member Functions
	eclMuMuEAlgorithm ()
	..Constructor
virtual	~eclMuMuEAlgorithm ()
	..Destructor
std::string	getPrefix () const
	Get the prefix used for getting calibration data.
bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type. More...
Calibration::ExpRun	convertPyExpRun (PyObject *pyObj)
	Performs the conversion of PyObject to ExpRun. More...
std::string	getCollectorName () const
	Alias for prefix. More...
void	setPrefix (const std::string &prefix)
	Set the prefix used to identify datastore objects.
void	setInputFileNames (PyObject *inputFileNames)
	Set the input file names used for this algorithm from a Python list. More...
PyObject *	getInputFileNames ()
	Get the input file names used for this algorithm and pass them out as a Python list of unicode strings.
std::vector< Calibration::ExpRun >	getRunListFromAllData () const
	Get the complete list of runs from inspection of collected data.
RunRange	getRunRangeFromAllData () const
	Get the complete RunRange from inspection of collected data.
IntervalOfValidity	getIovFromAllData () const
	Get the complete IoV from inspection of collected data.
void	fillRunToInputFilesMap ()
	Fill the mapping of ExpRun -> Files.
std::string	getGranularity () const
	Get the granularity of collected data.
EResult	execute (std::vector< Calibration::ExpRun > runs={}, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over vector of runs for a given iteration. More...
EResult	execute (PyObject *runs, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over Python list of runs. Converts to C++ and then calls the other execute() function.
std::list< Database::DBImportQuery > &	getPayloads ()
	Get constants (in TObjects) for database update from last execution.
std::list< Database::DBImportQuery >	getPayloadValues ()
	Get constants (in TObjects) for database update from last execution but passed by VALUE.
bool	commit ()
	Submit constants from last calibration into database.
bool	commit (std::list< Database::DBImportQuery > payloads)
	Submit constants from a (potentially previous) set of payloads.
const std::string &	getDescription () const
	Get the description of the algoithm (set by developers in constructor)
bool	loadInputJson (const std::string &jsonString)
	Load the m_inputJson variable from a string (useful from Python interface). The rturn bool indicates success or failure.
const std::string	dumpOutputJson () const
	Dump the JSON string of the output JSON object.
const std::vector< Calibration::ExpRun >	findPayloadBoundaries (std::vector< Calibration::ExpRun > runs, int iteration=0)
	Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.
template<>
std::shared_ptr< TTree >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Specialization of getObjectPtr<TTree>.

Public Attributes
int	cellIDLo
	..Parameters to control Novosibirsk fit to energy deposited in each crystal by mu+mu- events More...
int	cellIDHi
	Last cellID to be fit.
int	minEntries
	All crystals to be fit must have at least minEntries events in the fit range.
int	nToRebin
	If fewer entries than this, rebin and fix eta parameter.
double	tRatioMin
	entries/peak at low edge of fit must be greater than this
double	tRatioMax
	entries/peak at high edge of fit must be greater than this
double	lowerEdgeThresh
	Lower edge is where the fit = lowerEdgeThresh * peak value.
bool	performFits
	if false, input histograms are copied to output, but no fits are done.
bool	findExpValues
	if true, fits are used to find expected energy deposit for each crystal instead of the calibration constant
int	storeConst
	controls which values are written to the database. More...

Protected Member Functions
virtual EResult	calibrate () override
	..Run algorithm on events More...
void	setInputFileNames (std::vector< std::string > inputFileNames)
	Set the input file names used for this algorithm. More...
virtual bool	isBoundaryRequired (const Calibration::ExpRun &)
	Given the current collector data, make a decision about whether or not this run should be the start of a payload boundary.
virtual void	boundaryFindingSetup (std::vector< Calibration::ExpRun >, int)
	If you need to make some changes to your algorithm class before 'findPayloadBoundaries' is run, make them in this function.
virtual void	boundaryFindingTearDown ()
	Put your algorithm back into a state ready for normal execution if you need to.
const std::vector< Calibration::ExpRun > &	getRunList () const
	Get the list of runs for which calibration is called.
int	getIteration () const
	Get current iteration.
std::vector< std::string >	getVecInputFileNames () const
	Get the input file names used for this algorithm as a STL vector.
template<class T >
std::shared_ptr< T >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Get calibration data object by name and list of runs, the Merge function will be called to generate the overall object.
template<class T >
std::shared_ptr< T >	getObjectPtr (std::string name)
	Get calibration data object (for all runs the calibration is requested for) This function will only work during or after execute() has been called once.
template<>
shared_ptr< TTree >	getObjectPtr (const string &name, const vector< ExpRun > &requestedRuns)
	We cheekily cast the TChain to TTree for the returned pointer so that the user never knows Hopefully this doesn't cause issues if people do low level stuff to the tree...
std::string	getGranularityFromData () const
	Get the granularity of collected data.
void	saveCalibration (TClonesArray *data, const std::string &name)
	Store DBArray payload with given name with default IOV.
void	saveCalibration (TClonesArray *data, const std::string &name, const IntervalOfValidity &iov)
	Store DBArray with given name and custom IOV.
void	saveCalibration (TObject *data)
	Store DB payload with default name and default IOV.
void	saveCalibration (TObject *data, const IntervalOfValidity &iov)
	Store DB payload with default name and custom IOV.
void	saveCalibration (TObject *data, const std::string &name)
	Store DB payload with given name with default IOV.
void	saveCalibration (TObject *data, const std::string &name, const IntervalOfValidity &iov)
	Store DB payload with given name and custom IOV.
void	updateDBObjPtrs (const unsigned int event, const int run, const int experiment)
	Updates any DBObjPtrs by calling update(event) for DBStore.
void	setDescription (const std::string &description)
	Set algorithm description (in constructor)
void	clearCalibrationData ()
	Clear calibration data.
Calibration::ExpRun	getAllGranularityExpRun () const
	Returns the Exp,Run pair that means 'Everything'. Currently unused.
void	resetInputJson ()
	Clears the m_inputJson member variable.
void	resetOutputJson ()
	Clears the m_outputJson member variable.
template<class T >
void	setOutputJsonValue (const std::string &key, const T &value)
	Set a key:value pair for the outputJson object, expected to used interally during calibrate()
template<class T >
const T	getOutputJsonValue (const std::string &key) const
	Get a value using a key from the JSON output object, not sure why you would want to do this.
template<class T >
const T	getInputJsonValue (const std::string &key) const
	Get an input JSON value using a key. The normal exceptions are raised when the key doesn't exist.
const nlohmann::json &	getInputJsonObject () const
	Get the entire top level JSON object. We explicitly say this must be of object type so that we might pick.
bool	inputJsonKeyExists (const std::string &key) const
	Test for a key in the input JSON object.

Protected Attributes
std::vector< Calibration::ExpRun >	m_boundaries
	When using the boundaries functionality from isBoundaryRequired, this is used to store the boundaries. It is cleared when.

Private Member Functions
std::string	getExpRunString (Calibration::ExpRun &expRun) const
	Gets the "exp.run" string repr. of (exp,run)
std::string	getFullObjectPath (const std::string &name, Calibration::ExpRun expRun) const
	constructs the full TDirectory + Key name of an object in a TFile based on its name and exprun

Private Attributes
int	fitOK = 16
	fit is OK
int	iterations = 8
	fit reached max number of iterations, but is useable
int	noLowerEdge = 5
	could not determine lower edge of fit
int	atLimit = 4
	a parameter is at the limit; fit not useable
int	poorFit = 3
	low chi square; fit not useable
int	notFit = -1
	no fit performed
std::vector< std::string >	m_inputFileNames
	List of input files to the Algorithm, will initially be user defined but then gets the wildcards expanded during execute()
std::map< Calibration::ExpRun, std::vector< std::string > >	m_runsToInputFiles
	Map of Runs to input files. Gets filled when you call getRunRangeFromAllData, gets cleared when setting input files again.
std::string	m_granularityOfData
	Granularity of input data. This only changes when the input files change so it isn't specific to an execution.
ExecutionData	m_data
	Data specific to a SINGLE execution of the algorithm. Gets reset at the beginning of execution.
std::string	m_description {""}
	Description of the algorithm.
std::string	m_prefix {""}
	The name of the TDirectory the collector objects are contained within.
nlohmann::json	m_jsonExecutionInput = nlohmann::json::object()
	Optional input JSON object used to make decisions about how to execute the algorithm code.
nlohmann::json	m_jsonExecutionOutput = nlohmann::json::object()
	Optional output JSON object that can be set during the execution by the underlying algorithm code.

Static Private Attributes
static const Calibration::ExpRun	m_allExpRun = make_pair(-1, -1)
	allExpRun

Detailed Description

Calibrate ecl crystals using muon pair events.

Definition at line 22 of file eclMuMuEAlgorithm.h.

Member Enumeration Documentation

EResult

enum EResult

inherited

The result of calibration.

Enumerator
c_OK	Finished successfuly =0 in Python.
c_Iterate	Needs iteration =1 in Python.
c_NotEnoughData	Needs more data =2 in Python.
c_Failure	Failed =3 in Python.
c_Undefined	Not yet known (before execution) =4 in Python.

Definition at line 40 of file CalibrationAlgorithm.h.

Member Function Documentation

calibrate()

CalibrationAlgorithm::EResult calibrate ( )

overrideprotectedvirtual

..Run algorithm on events

---

ranges of various fit parameters, and tolerance to determine that fit is at the limit

Put root into batch mode so that we don't try to open a graphics window

---

Write out the job parameters

---

Clean up existing histograms if necessary

---

Histograms containing the data collected by eclMuMuECollectorModule

---

Record the number of entries per crystal in the normalized energy histogram and calculate the average expected energy per crystal and calibration constants from Collector

---

Write out the basic histograms in all cases

---

If we have not been asked to do fits, we can quit now

---

Check that every crystal has enough entries.

Insufficient data. Quit if we are required to have a successful fit for every crystal

---

Some prep for the many fits about to follow

histograms to store results for database

Diagnostic histograms

1D summary histograms

---

Fits are requested and there is sufficient data. Loop over specified crystals and performs fits to the amplitude distributions

Project 1D histogram of energy in this crystal

Fit function (xmin, xmax, nparameters) for this histogram

Estimate initial parameters from the histogram. For peak, use maximum bin in the allowed range

eta is nominal values

---

Fit

Set the initial parameters

Fit

bins on either side of this value

look for the target value between these two points

---

Fit status

did not find lower edge

poor fit

parameter at limit

fill diagnostic histograms

1D summary histograms

Store histogram with fit

---

Interpret results of fit as expected energy or calibration constant

if the fit is not successful, set peakE and edge to -1, so that output calib = -1 * abs(input calib)

Find expected energies from MC, if requested

Otherwise, calibration constant

---

Write output to DB if requested and successful

Store expected energies

Store calibration constants

---

Write out diagnostic histograms

Histograms containing values written to DB

---

Clean up histograms in case Algorithm is called again

---

Set the return code appropriately

Implements CalibrationAlgorithm.

Definition at line 65 of file eclMuMuEAlgorithm.cc.

{
  double limitTol = 0.0005; /*< tolerance for checking if a parameter is at the limit */
  double minFitLimit = 1e-25; /*< cut off for labeling a fit as poor */
  double peakMin(0.4), peakMax(2.2); /*< range for peak of normalized energy distribution */
  double peakTol = limitTol * (peakMax - peakMin); /*< fit is at limit if it is within peakTol of min or max */
  double effSigMin(0.02), effSigMax(0.2); /*< range for effective sigma of normalized energy distribution */
  double effSigTol = limitTol * (effSigMax - effSigMin); /*< fit is at limit if it is within effSigTol of min or max */
  double etaNom(-0.41); /*< Nominal tail parameter; fixed to this value for low statistics fits */
  double etaMin(-1.), etaMax(0.); /*< Novosibirsk tail parameter range */
  double etaTol = limitTol * (etaMax - etaMin); /*< fit is at limit if it is within etaTol of min or max */
 
  gROOT->SetBatch();
 
  B2INFO("eclMuMuEAlgorithm parameters:");
  B2INFO("cellIDLo = " << cellIDLo);
  B2INFO("cellIDHi = " << cellIDHi);
  B2INFO("minEntries = " << minEntries);
  B2INFO("tRatioMin = " << tRatioMin);
  B2INFO("tRatioMax = " << tRatioMax);
  B2INFO("lowerEdgeThresh = " << lowerEdgeThresh);
  B2INFO("performFits = " << performFits);
  B2INFO("findExpValues = " << findExpValues);
  B2INFO("storeConst = " << storeConst);
 
  TH1F* dummy;
  dummy = (TH1F*)gROOT->FindObject("IntegralVsCrysID");
  if (dummy) {delete dummy;}
  dummy = (TH1F*)gROOT->FindObject("AverageExpECrys");
  if (dummy) {delete dummy;}
  dummy = (TH1F*)gROOT->FindObject("AverageElecCalib");
  if (dummy) {delete dummy;}
  dummy = (TH1F*)gROOT->FindObject("AverageInitCalib");
  if (dummy) {delete dummy;}
 
  auto TrkPerCrysID = getObjectPtr<TH1F>("TrkPerCrysID");
  auto EnVsCrysID = getObjectPtr<TH2F>("EnVsCrysID");
  auto ExpEvsCrys = getObjectPtr<TH1F>("ExpEvsCrys");
  auto ElecCalibvsCrys = getObjectPtr<TH1F>("ElecCalibvsCrys");
  auto InitialCalibvsCrys = getObjectPtr<TH1F>("InitialCalibvsCrys");
  auto CalibEntriesvsCrys = getObjectPtr<TH1F>("CalibEntriesvsCrys");
  auto RawDigitAmpvsCrys = getObjectPtr<TH2F>("RawDigitAmpvsCrys");
  auto RawDigitTimevsCrys = getObjectPtr<TH2F>("RawDigitTimevsCrys");
  auto hitCrysVsExtrapolatedCrys = getObjectPtr<TH2F>("hitCrysVsExtrapolatedCrys");
 
  TH1F* IntegralVsCrysID = new TH1F("IntegralVsCrysID", "Integral of EnVsCrysID for each crystal;crystal ID;Entries",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* AverageExpECrys = new TH1F("AverageExpECrys", "Average expected E per crys from collector;Crystal ID;Energy (GeV)",
                                   ECLElementNumbers::c_NCrystals, 0,
                                   ECLElementNumbers::c_NCrystals);
  TH1F* AverageElecCalib = new TH1F("AverageElecCalib", "Average electronics calib const vs crystal;Crystal ID;Calibration constant",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* AverageInitCalib = new TH1F("AverageInitCalib", "Average initial calib const vs crystal;Crystal ID;Calibration constant",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
 
  for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
    TH1D* hEnergy = EnVsCrysID->ProjectionY("hEnergy", crysID + 1, crysID + 1);
    int Integral = hEnergy->Integral();
    IntegralVsCrysID->SetBinContent(crysID + 1, Integral);
 
    double TotEntries = CalibEntriesvsCrys->GetBinContent(crysID + 1);
 
    double expectedE = 0.;
    if (TotEntries > 0.) {expectedE = ExpEvsCrys->GetBinContent(crysID + 1) / TotEntries;}
    AverageExpECrys->SetBinContent(crysID + 1, expectedE);
 
    double calibconst = 0.;
    if (TotEntries > 0.) {calibconst = ElecCalibvsCrys->GetBinContent(crysID + 1) / TotEntries;}
    AverageElecCalib->SetBinContent(crysID + 1, calibconst);
 
    calibconst = 0.;
    if (TotEntries > 0.) {calibconst = InitialCalibvsCrys->GetBinContent(crysID + 1) / TotEntries;}
    AverageInitCalib->SetBinContent(crysID + 1, calibconst);
  }
 
  TFile* histfile = new TFile("eclMuMuEAlgorithm.root", "recreate");
  TrkPerCrysID->Write();
  EnVsCrysID->Write();
  IntegralVsCrysID->Write();
  AverageExpECrys->Write();
  AverageElecCalib->Write();
  AverageInitCalib->Write();
  RawDigitAmpvsCrys->Write();
  RawDigitTimevsCrys->Write();
  hitCrysVsExtrapolatedCrys->Write();
 
  if (!performFits) {
    B2RESULT("eclMuMuEAlgorithm has not been asked to perform fits; copying input histograms and quitting");
    histfile->Close();
    return c_NotEnoughData;
  }
 
  bool sufficientData = true;
  for (int crysID = cellIDLo - 1; crysID < cellIDHi; crysID++) {
    if (IntegralVsCrysID->GetBinContent(crysID + 1) < minEntries) {
      if (storeConst == 1) {B2RESULT("eclMuMuEAlgorithm: crystal " << crysID << " has insufficient statistics: " << IntegralVsCrysID->GetBinContent(crysID + 1) << ". Requirement is " << minEntries);}
      sufficientData = false;
      break;
    }
  }
 
  if (!sufficientData && storeConst == 1) {
    histfile->Close();
    return c_NotEnoughData;
  }
 
  TH1F* CalibVsCrysID = new TH1F("CalibVsCrysID", "Calibration constant vs crystal ID;crystal ID;counts per GeV",
                                 ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* ExpEnergyperCrys = new TH1F("ExpEnergyperCrys", "Expected energy per crystal;Crystal ID;Peak energy (GeV)",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
 
  TH1F* PeakVsCrysID = new TH1F("PeakVsCrysID", "Peak of Novo fit vs crystal ID;crystal ID;Peak normalized energy",
                                ECLElementNumbers::c_NCrystals, 0,
                                ECLElementNumbers::c_NCrystals);
  TH1F* EdgeVsCrysID = new TH1F("EdgeVsCrysID", "Lower edge of Novo fit vs crystal ID;crystal ID", ECLElementNumbers::c_NCrystals, 0,
                                ECLElementNumbers::c_NCrystals);
  TH1F* effSigVsCrysID = new TH1F("effSigVsCrysID", "effSigma vs crystal ID;crystal ID;sigma)", ECLElementNumbers::c_NCrystals, 0,
                                  ECLElementNumbers::c_NCrystals);
  TH1F* etaVsCrysID = new TH1F("etaVsCrysID", "eta vs crystal ID;crystal ID;Novo eta parameter", ECLElementNumbers::c_NCrystals, 0,
                               ECLElementNumbers::c_NCrystals);
  TH1F* normVsCrysID = new TH1F("normVsCrysID", "Novosibirsk normalization vs crystal ID;crystal ID;normalization",
                                ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* lowerLimitVsCrysID = new TH1F("lowerLimitVsCrysID", "fit range lower limit vs crystal ID;crystal ID;lower fit limit",
                                      ECLElementNumbers::c_NCrystals, 0,
                                      ECLElementNumbers::c_NCrystals);
  TH1F* fitLimitVsCrysID = new TH1F("fitLimitVsCrysID", "fit range upper limit vs crystal ID;crystal ID;upper fit limit",
                                    ECLElementNumbers::c_NCrystals, 0,
                                    ECLElementNumbers::c_NCrystals);
  TH1F* StatusVsCrysID = new TH1F("StatusVsCrysID", "Fit status vs crystal ID;crystal ID;Fit status", ECLElementNumbers::c_NCrystals,
                                  0, ECLElementNumbers::c_NCrystals);
 
  TH1F* hStatus = new TH1F("hStatus", "Fit status", 25, -5, 20);
  TH1F* hPeak = new TH1F("hPeak", "Peaks of normalized energy distributions, successful fits;Peak of Novosibirsk fit", 200, 0.8, 1.2);
  TH1F* fracPeakUnc = new TH1F("fracPeakUnc", "Fractional uncertainty on peak uncertainty, successful fits;Uncertainty on peak", 100,
                               0, 0.1);
 
 
  bool allFitsOK = true;
  for (int crysID = cellIDLo - 1; crysID < cellIDHi; crysID++) {
 
    TString name = "Enormalized";
    name += crysID;
    TH1D* hEnergy = EnVsCrysID->ProjectionY(name, crysID + 1, crysID + 1);
 
    double histMin = hEnergy->GetXaxis()->GetXmin();
    double histMax = hEnergy->GetXaxis()->GetXmax();
    TF1* func = new TF1("eclNovoConst", eclNovoConst, histMin, histMax, 5);
    func->SetParNames("normalization", "peak", "effSigma", "eta", "const");
    func->SetParLimits(1, peakMin, peakMax);
    func->SetParLimits(2, effSigMin, effSigMax);
    func->SetParLimits(3, etaMin, etaMax);
 
    //..Currently not using the constant term
    double constant = 0.;
    func->FixParameter(4, constant);
 
    //..If low statistics, rebin, and fix eta
    if (hEnergy->GetEntries() < nToRebin) {
      hEnergy->Rebin(2);
      func->FixParameter(3, etaNom);
    }
 
    hEnergy->GetXaxis()->SetRangeUser(peakMin, peakMax);
    double peak = hEnergy->GetMaximum();
    int maxBin = hEnergy->GetMaximumBin();
    double peakE = hEnergy->GetBinLowEdge(maxBin);
    double peakEUnc = 0.;
    double normalization = hEnergy->GetMaximum();
    double normUnc = 0.;
    double effSigma = hEnergy->GetRMS();
    double sigmaUnc = 0.;
    hEnergy->GetXaxis()->SetRangeUser(histMin, histMax);
    double fitProb = 0.;
 
    double eta = etaNom;
    double etaUnc = 0.;
 
    //..Will find the lower edge of normalized energy at the end of the fit
    double lowerEnEdge = 0.;
 
    //..Fit range from set of bins with sufficient entries.
    double targetY = tRatioMin * peak;
    int iLow = maxBin;
    while (hEnergy->GetBinContent(iLow) > targetY) {iLow--;}
    double fitlow = hEnergy->GetBinLowEdge(iLow);
 
    targetY = tRatioMax * peak;
    int iHigh = maxBin;
    while (hEnergy->GetBinContent(iHigh) > targetY) {iHigh++;}
    double fithigh = hEnergy->GetBinLowEdge(iHigh + 1);
 
    bool fitHist = IntegralVsCrysID->GetBinContent(crysID + 1) >= minEntries; /* fit only if enough events */
    if (fitHist) {
 
      func->SetParameters(normalization, peakE, effSigma, eta, constant);
 
      hEnergy->Fit(func, "LIQ", "", fitlow, fithigh);
      normalization = func->GetParameter(0);
      normUnc = func->GetParError(0);
      peakE = func->GetParameter(1);
      peakEUnc = func->GetParError(1);
      effSigma = func->GetParameter(2);
      sigmaUnc = func->GetParError(2);
      eta = func->GetParameter(3);
      etaUnc = func->GetParError(3);
      fitProb = func->GetProb();
 
      //..Lower edge of the fit function is used to find the calibration constant.
      //  Can now use the peak of the fit, instead of the bin content.
      peak = func->Eval(peakE);
      targetY = lowerEdgeThresh * peak;
 
      iHigh = hEnergy->GetXaxis()->FindBin(peakE) + 1;
      iLow = hEnergy->GetXaxis()->FindBin(fitlow);
      int iLast = iHigh;
      for (int ibin = iHigh; ibin > iLow; ibin--) {
        double xc = hEnergy->GetBinCenter(ibin);
        if (func->Eval(xc) > targetY) {iLast = ibin;}
      }
      double xHigh = hEnergy->GetBinCenter(iLast);
      double xLow = hEnergy->GetBinCenter(iLast - 1);
 
      if (func->Eval(xLow) < targetY and func->Eval(xHigh) > targetY) {
        func->SetNpx(1000);
        lowerEnEdge = func->GetX(targetY, xLow, xHigh);
      }
    }
 
    int iStatus = fitOK; // success
 
    if (lowerEnEdge < 0.01) {iStatus = noLowerEdge;}
 
    if (fitProb <= minFitLimit) {iStatus = poorFit;}
 
    if ((peakE < peakMin + peakTol) || (peakE > peakMax - peakTol)) {iStatus = atLimit;}
    if ((effSigma < effSigMin + effSigTol) || (effSigma > effSigMax - effSigTol)) {iStatus = atLimit;}
    if ((eta < etaMin + etaTol) || (eta > etaMax - etaTol)) {iStatus = atLimit;}
 
    //** No fit
    if (!fitHist) {iStatus = notFit;} // not fit
 
    int histbin = crysID + 1;
    PeakVsCrysID->SetBinContent(histbin, peakE);
    PeakVsCrysID->SetBinError(histbin, peakEUnc);
    EdgeVsCrysID->SetBinContent(histbin, lowerEnEdge);
    EdgeVsCrysID->SetBinError(histbin, peakEUnc); // approximate
    effSigVsCrysID->SetBinContent(histbin, effSigma);
    effSigVsCrysID->SetBinError(histbin, sigmaUnc);
    etaVsCrysID->SetBinContent(histbin, eta);
    etaVsCrysID->SetBinError(histbin, etaUnc);
    normVsCrysID->SetBinContent(histbin, normalization);
    normVsCrysID->SetBinError(histbin, normUnc);
    lowerLimitVsCrysID->SetBinContent(histbin, fitlow);
    lowerLimitVsCrysID->SetBinError(histbin, 0);
    fitLimitVsCrysID->SetBinContent(histbin, fithigh);
    fitLimitVsCrysID->SetBinError(histbin, 0);
    StatusVsCrysID->SetBinContent(histbin, iStatus);
    StatusVsCrysID->SetBinError(histbin, 0);
 
    hStatus->Fill(iStatus);
    if (iStatus >= iterations) {
      hPeak->Fill(peakE);
      fracPeakUnc->Fill(peakEUnc / peakE);
    }
 
    B2INFO("cellID " << crysID + 1 << " status = "  << iStatus << " fit probability = " << fitProb);
    histfile->cd();
    hEnergy->Write();
 
  } /* end of loop over crystals */
 
  for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
    int histbin = crysID + 1;
    double fitstatus = StatusVsCrysID->GetBinContent(histbin);
    double peakE = PeakVsCrysID->GetBinContent(histbin);
    double edge = EdgeVsCrysID->GetBinContent(histbin);
    double fracpeakEUnc = PeakVsCrysID->GetBinError(histbin) / peakE;
 
    if (fitstatus < iterations) {
      peakE = -1.;
      edge = -1.;
      fracpeakEUnc = 0.;
      if (histbin >= cellIDLo && histbin <= cellIDHi) {
        B2RESULT("eclMuMuEAlgorithm: cellID " << histbin << " is not a successful fit. Status = " << fitstatus);
        allFitsOK = false;
      }
    }
 
    if (findExpValues) {
      double inputExpE = abs(AverageExpECrys->GetBinContent(histbin));
      ExpEnergyperCrys->SetBinContent(histbin, inputExpE * edge);
      ExpEnergyperCrys->SetBinError(histbin, fracpeakEUnc * inputExpE * peakE);
    } else {
 
      double inputCalib = abs(AverageInitCalib->GetBinContent(histbin));
      CalibVsCrysID->SetBinContent(histbin, inputCalib / edge);
      CalibVsCrysID->SetBinError(histbin, fracpeakEUnc * inputCalib / peakE);
    }
  }
 
  bool DBsuccess = false;
  if (storeConst == 0 || (storeConst == 1 && allFitsOK)) {
    DBsuccess = true;
    if (findExpValues) {
 
      std::vector<float> tempE;
      std::vector<float> tempUnc;
      for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
        tempE.push_back(ExpEnergyperCrys->GetBinContent(crysID + 1));
        tempUnc.push_back(ExpEnergyperCrys->GetBinError(crysID + 1));
      }
      ECLCrystalCalib* ExpectedE = new ECLCrystalCalib();
      ExpectedE->setCalibVector(tempE, tempUnc);
      saveCalibration(ExpectedE, "ECLExpMuMuE");
      B2RESULT("eclCosmicEAlgorithm: successfully stored expected energies ECLExpMuMuE");
 
    } else {
 
      std::vector<float> tempCalib;
      std::vector<float> tempCalibUnc;
      for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
        tempCalib.push_back(CalibVsCrysID->GetBinContent(crysID + 1));
        tempCalibUnc.push_back(CalibVsCrysID->GetBinError(crysID + 1));
      }
      ECLCrystalCalib* MuMuECalib = new ECLCrystalCalib();
      MuMuECalib->setCalibVector(tempCalib, tempCalibUnc);
      saveCalibration(MuMuECalib, "ECLCrystalEnergyMuMu");
      B2RESULT("eclMuMuEAlgorithm: successfully stored ECLCrystalEnergyMuMu calibration constants");
    }
  }
 
  PeakVsCrysID->Write();
  EdgeVsCrysID->Write();
  effSigVsCrysID->Write();
  etaVsCrysID->Write();
  normVsCrysID->Write();
  lowerLimitVsCrysID->Write();
  fitLimitVsCrysID->Write();
  StatusVsCrysID->Write();
  hPeak->Write();
  fracPeakUnc->Write();
  hStatus->Write();
 
  if (findExpValues) {
    ExpEnergyperCrys->Write();
  } else {
    CalibVsCrysID->Write();
  }
  histfile->Close();
 
  dummy = (TH1F*)gROOT->FindObject("PeakVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("EdgeVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("effSigVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("etaVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("normVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("lowerLimitVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("fitLimitVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("StatusVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("fitProbSame"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("fracPeakUnc"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("hStatus"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("ExpEnergyperCrys"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("CalibVsCrysID"); delete dummy;
 
 
  if (storeConst == -1) {
    B2RESULT("eclMuMuEAlgorithm performed fits but was not asked to store contants");
    return c_Failure;
  } else if (!DBsuccess) {
    if (findExpValues) { B2RESULT("eclMuMuEAlgorithm: failed to store expected values"); }
    else { B2RESULT("eclMuMuEAlgorithm: failed to store calibration constants"); }
    return c_Failure;
  }
  return c_OK;
}

checkPyExpRun()

bool checkPyExpRun ( PyObject *  pyObj )

inherited

Checks that a PyObject can be successfully converted to an ExpRun type.

Checks if the PyObject can be converted to ExpRun.

Definition at line 28 of file CalibrationAlgorithm.cc.

convertPyExpRun()

ExpRun convertPyExpRun ( PyObject *  pyObj )

inherited

Performs the conversion of PyObject to ExpRun.

Converts the PyObject to an ExpRun. We've preoviously checked the object so this assumes a lot about the PyObject.

Definition at line 70 of file CalibrationAlgorithm.cc.

execute()

CalibrationAlgorithm::EResult execute ( std::vector< Calibration::ExpRun >  runs = {}, int  iteration = 0, IntervalOfValidity  iov = IntervalOfValidity()  )

inherited

Runs calibration over vector of runs for a given iteration.

You can also specify the IoV to save the database payload as. By default the Algorithm will create an IoV from your requested ExpRuns, or from the overall ExpRuns of the input data if you haven't specified ExpRuns in this function.

No checks are performed to make sure that a IoV you specify matches the data you ran over, it simply labels the IoV to commit to the database later.

Definition at line 114 of file CalibrationAlgorithm.cc.

getCollectorName()

std::string getCollectorName ( ) const

inlineinherited

Alias for prefix.

For convenience and less writing, we say developers to set this to default collector module name in constructor of base class. One can however use the dublets of collector+algorithm multiple times with different settings. To bind these together correctly, the prefix has to be set the same for algo and collector. So we call the setter setPrefix rather than setModuleName or whatever. This getter will work out of the box for default cases -> return the name of module you have to add to your path to collect data for this algorihtm.

Definition at line 164 of file CalibrationAlgorithm.h.

setInputFileNames() [1/2]

void setInputFileNames ( PyObject *  inputFileNames )

inherited

Set the input file names used for this algorithm from a Python list.

Set the input file names used for this algorithm and resolve the wildcards.

Definition at line 166 of file CalibrationAlgorithm.cc.

setInputFileNames() [2/2]

void setInputFileNames ( std::vector< std::string >  inputFileNames )

protectedinherited

Set the input file names used for this algorithm.

Set the input file names used for this algorithm and resolve the wildcards.

Definition at line 194 of file CalibrationAlgorithm.cc.

Member Data Documentation

cellIDLo

int cellIDLo

..Parameters to control Novosibirsk fit to energy deposited in each crystal by mu+mu- events

First cellID to be fit

Definition at line 32 of file eclMuMuEAlgorithm.h.

storeConst

int storeConst

controls which values are written to the database.

0 (default): store value found by successful fits, or -|input value| otherwise; -1 : do not store values 1 : store values if every fit for [cellIDLo,cellIDHi] was successful

Definition at line 41 of file eclMuMuEAlgorithm.h.

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The documentation for this class was generated from the following files:

• ecl/calibration/include/eclMuMuEAlgorithm.h
• ecl/calibration/src/eclMuMuEAlgorithm.cc